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Running
on
Zero
File size: 6,092 Bytes
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from src.utils.mp_utils import LMKExtractor
from src.utils.draw_utils import FaceMeshVisualizer
from src.utils.img_utils import pil_to_cv2, cv2_to_pil, center_crop_cv2, pils_from_video, save_videos_from_pils, save_video_from_cv2_list
from PIL import Image
import cv2
from IPython import embed
import numpy as np
import copy
from src.utils.motion_utils import motion_sync
import pathlib
import torch
import pickle
from glob import glob
import os
vis = FaceMeshVisualizer(draw_iris=False, draw_mouse=True, draw_eye=True, draw_nose=True, draw_eyebrow=True, draw_pupil=True)
imsize = (512, 512)
visualization = True
driver_video = "./assets/driven_videos/a.mp4"
# driver_videos = glob("/nas2/luoque.lym/evaluation/test_datasets/gt_data/OurDataset/*.mp4")
ref_image = './assets/test_imgs/d.png'
# ref_image = 'panda.png'
lmk_extractor = LMKExtractor()
input_frames_cv2 = [cv2.resize(center_crop_cv2(pil_to_cv2(i)), imsize) for i in pils_from_video(driver_video)]
ref_frame =cv2.resize(cv2.imread(ref_image), (512, 512))
ref_det = lmk_extractor(ref_frame)
# print(ref_det)
sequence_driver_det = []
try:
for frame in input_frames_cv2:
result = lmk_extractor(frame)
assert result is not None, "{}, bad video, face not detected".format(driver_video)
sequence_driver_det.append(result)
except:
print("face detection failed")
exit()
print(len(sequence_driver_det))
if visualization:
pose_frames_driver = [vis.draw_landmarks((512, 512), i["lmks"], normed=True) for i in sequence_driver_det]
poses_add_driver = [(i * 0.5 + j * 0.5).clip(0,255).astype(np.uint8) for i, j in zip(input_frames_cv2, pose_frames_driver)]
save_dir = './{}'.format(ref_image.split('/')[-1].replace('.png', ''))
os.makedirs(save_dir, exist_ok=True)
sequence_det_ms = motion_sync(sequence_driver_det, ref_det)
for i in range(len(sequence_det_ms)):
with open('{}/{}.pkl'.format(save_dir, i), 'wb') as file:
pickle.dump(sequence_det_ms[i], file)
if visualization:
pose_frames = [vis.draw_landmarks((512, 512), i, normed=False) for i in sequence_det_ms]
poses_add = [(i * 0.5 + ref_frame * 0.5).clip(0,255).astype(np.uint8) for i in pose_frames]
# sequence_det_ms = motion_sync(sequence_driver_det, ref_det, per_landmark_align=False)
# for i in range(len(sequence_det_ms)):
# tmp = {}
# tmp["lmks"] = sequence_det_ms[i]
# with open('{}_v2/{}.pkl'.format(save_dir, i), 'wb') as file:
# pickle.dump(tmp, file)
# pose_frames_wo_lmkalign = [vis.draw_landmarks((512, 512), i, normed=False) for i in sequence_det_ms]
# poses_add_wo_lmkalign = [(i * 0.5 + ref_frame * 0.5).clip(0,255).astype(np.uint8) for i in pose_frames_wo_lmkalign]
poses_cat = [np.concatenate([i, j], axis=1) for i, j in zip(poses_add_driver, poses_add)]
save_video_from_cv2_list(poses_cat, "./vis_example.mp4", fps=24.0)
# for ref_image in ref_images[:1]:
# # for driver_video in driver_videos:
# # ref_image = "./samples/007.png"
# # save_dir = '/nas2/jiajiong.caojiajio/data/test_pose/OurDataset/{}'.format(driver_video.split('/')[-1].replace('.mp4', ''))
# save_dir = './{}'.format(ref_image.split('/')[-1].replace('.png', ''))
# os.makedirs(save_dir+'_v1', exist_ok=True)
# os.makedirs(save_dir+'_v2', exist_ok=True)
# #"./samples/hedra_003.png"
# #"./samples/video_temp_fix.mov"
# input_frames_cv2 = [cv2.resize(center_crop_cv2(pil_to_cv2(i)), imsize) for i in pils_from_video(driver_video)]
# # input_frames_cv2 = [cv2.resize(pil_to_cv2(i), imsize) for i in pils_from_video(driver_video)]
# lmk_extractor = LMKExtractor()
# ref_frame =cv2.resize(cv2.imread(ref_image), (512, 512))
# ref_det = lmk_extractor(ref_frame)
# sequence_driver_det = []
# try:
# for frame in input_frames_cv2:
# result = lmk_extractor(frame)
# assert result is not None, "{}, bad video, face not detected".format(driver_video)
# sequence_driver_det.append(result)
# except:
# continue
# print(len(sequence_driver_det))
# # os.makedirs(save_dir, exist_ok=True)
# # for i in range(len(sequence_driver_det)):
# # with open('{}/{}.pkl'.format(save_dir, i), 'wb') as file:
# # pickle.dump(sequence_driver_det[i]["lmks"] * imsize[0], file)
# #[vis.draw_landmarks(imsize, i["lmks"], normed=True, white=True) for i in det_results]
# pose_frames_driver = [vis.draw_landmarks((512, 512), i["lmks"], normed=True) for i in sequence_driver_det]
# poses_add_driver = [(i * 0.5 + j * 0.5).clip(0,255).astype(np.uint8) for i, j in zip(input_frames_cv2, pose_frames_driver)]
# sequence_det_ms = motion_sync(sequence_driver_det, ref_det)
# for i in range(len(sequence_det_ms)):
# tmp = {}
# tmp["lmks"] = sequence_det_ms[i]
# with open('{}_v1/{}.pkl'.format(save_dir, i), 'wb') as file:
# pickle.dump(tmp, file)
# pose_frames = [vis.draw_landmarks((512, 512), i, normed=False) for i in sequence_det_ms]
# poses_add = [(i * 0.5 + ref_frame * 0.5).clip(0,255).astype(np.uint8) for i in pose_frames]
# sequence_det_ms = motion_sync(sequence_driver_det, ref_det, per_landmark_align=False)
# for i in range(len(sequence_det_ms)):
# tmp = {}
# tmp["lmks"] = sequence_det_ms[i]
# with open('{}_v2/{}.pkl'.format(save_dir, i), 'wb') as file:
# pickle.dump(tmp, file)
# pose_frames_wo_lmkalign = [vis.draw_landmarks((512, 512), i, normed=False) for i in sequence_det_ms]
# poses_add_wo_lmkalign = [(i * 0.5 + ref_frame * 0.5).clip(0,255).astype(np.uint8) for i in pose_frames_wo_lmkalign]
# poses_cat = [np.concatenate([i, j, k], axis=1) for i, j, k in zip(poses_add_driver, poses_add_wo_lmkalign, poses_add)]
# save_video_from_cv2_list(poses_cat, "./output/example2.mp4", fps=24.0)
# # exit()
# #embed()
# #poses_cat = [(i * 0.5 + j * 0.5).clip(0,255).astype(np.uint8) for i, j in zip(input_frames_cv2, pose_frames)]
# #save_videos_from_pils([cv2_to_pil(i) for i in poses_cat], "./output/pose_cat.mp4", fps=24) |